Benchmarking reports

from sklearn_benchmarks.report import Reporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

reporting = Reporting(config_file_path="config.yml")
reporting.run()

Time report

	hour	min	sec
algo
KNeighborsClassifier	0.0	13.0	40.302472
daal4py_KNeighborsClassifier	0.0	5.0	16.969540
KNeighborsClassifier_kd_tree	0.0	6.0	39.341896
daal4py_KNeighborsClassifier_kd_tree	0.0	1.0	50.069258
KMeans_tall	0.0	1.0	47.681629
daal4py_KMeans_tall	0.0	1.0	18.703511
KMeans_short	0.0	0.0	25.815144
daal4py_KMeans_short	0.0	0.0	12.654353
LogisticRegression	0.0	1.0	7.733477
daal4py_LogisticRegression	0.0	1.0	1.248076
Ridge	0.0	0.0	1.095413
daal4py_Ridge	0.0	0.0	0.830633
total	0.0	33.0	22.534776

KNeighborsClassifier vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier	fit	0.147	0.003	1000000	1000000	100	brute	-1	1	NaN	NaN	0.495	0.005	0.297	0.000	See
1	KNeighborsClassifier	predict	0.197	0.017	1000000	1	100	brute	-1	1	1.0	1.0	0.112	0.008	1.749	0.012	See
2	KNeighborsClassifier	predict	30.543	0.000	1000000	1000	100	brute	-1	1	1.0	1.0	3.855	0.028	7.923	0.000	See
3	KNeighborsClassifier	fit	0.143	0.003	1000000	1000000	100	brute	-1	5	NaN	NaN	0.500	0.014	0.285	0.001	See
4	KNeighborsClassifier	predict	0.190	0.011	1000000	1	100	brute	-1	5	1.0	1.0	0.115	0.005	1.651	0.005	See
5	KNeighborsClassifier	predict	38.888	0.000	1000000	1000	100	brute	-1	5	1.0	1.0	3.909	0.035	9.949	0.000	See
6	KNeighborsClassifier	fit	0.137	0.003	1000000	1000000	100	brute	-1	100	NaN	NaN	0.500	0.011	0.274	0.001	See
7	KNeighborsClassifier	predict	0.220	0.015	1000000	1	100	brute	-1	100	1.0	1.0	0.107	0.005	2.048	0.007	See
8	KNeighborsClassifier	predict	38.635	0.000	1000000	1000	100	brute	-1	100	1.0	1.0	3.988	0.046	9.688	0.000	See
9	KNeighborsClassifier	fit	0.135	0.003	1000000	1000000	100	brute	1	1	NaN	NaN	0.503	0.008	0.268	0.001	See
10	KNeighborsClassifier	predict	0.219	0.054	1000000	1	100	brute	1	1	1.0	1.0	0.112	0.009	1.954	0.067	See
11	KNeighborsClassifier	predict	15.811	0.118	1000000	1000	100	brute	1	1	1.0	1.0	3.834	0.023	4.124	0.000	See
12	KNeighborsClassifier	fit	0.131	0.003	1000000	1000000	100	brute	1	5	NaN	NaN	0.495	0.005	0.266	0.001	See
13	KNeighborsClassifier	predict	0.202	0.003	1000000	1	100	brute	1	5	1.0	1.0	0.103	0.003	1.963	0.001	See
14	KNeighborsClassifier	predict	24.810	0.116	1000000	1000	100	brute	1	5	1.0	1.0	3.855	0.049	6.436	0.000	See
15	KNeighborsClassifier	fit	0.143	0.005	1000000	1000000	100	brute	1	100	NaN	NaN	0.496	0.009	0.289	0.001	See
16	KNeighborsClassifier	predict	0.206	0.003	1000000	1	100	brute	1	100	1.0	1.0	0.102	0.001	2.011	0.000	See
17	KNeighborsClassifier	predict	24.407	0.018	1000000	1000	100	brute	1	100	1.0	1.0	3.910	0.058	6.242	0.000	See
18	KNeighborsClassifier	fit	0.058	0.001	1000000	1000000	2	brute	-1	1	NaN	NaN	0.106	0.005	0.545	0.002	See
19	KNeighborsClassifier	predict	0.021	0.002	1000000	1	2	brute	-1	1	1.0	0.0	0.004	0.000	4.871	0.013	See
20	KNeighborsClassifier	predict	24.378	0.048	1000000	1000	2	brute	-1	1	1.0	0.0	0.818	0.016	29.798	0.000	See
21	KNeighborsClassifier	fit	0.057	0.001	1000000	1000000	2	brute	-1	5	NaN	NaN	0.098	0.003	0.580	0.002	See
22	KNeighborsClassifier	predict	0.031	0.001	1000000	1	2	brute	-1	5	1.0	1.0	0.004	0.000	7.283	0.012	See
23	KNeighborsClassifier	predict	32.901	0.000	1000000	1000	2	brute	-1	5	1.0	1.0	0.823	0.016	39.973	0.000	See
24	KNeighborsClassifier	fit	0.059	0.002	1000000	1000000	2	brute	-1	100	NaN	NaN	0.105	0.006	0.562	0.004	See
25	KNeighborsClassifier	predict	0.033	0.002	1000000	1	2	brute	-1	100	1.0	1.0	0.004	0.000	7.327	0.009	See
26	KNeighborsClassifier	predict	32.547	0.000	1000000	1000	2	brute	-1	100	1.0	1.0	0.889	0.023	36.597	0.001	See
27	KNeighborsClassifier	fit	0.058	0.001	1000000	1000000	2	brute	1	1	NaN	NaN	0.114	0.006	0.510	0.003	See
28	KNeighborsClassifier	predict	0.016	0.001	1000000	1	2	brute	1	1	1.0	0.0	0.005	0.000	3.417	0.008	See
29	KNeighborsClassifier	predict	10.926	0.050	1000000	1000	2	brute	1	1	1.0	0.0	0.885	0.157	12.342	0.031	See
30	KNeighborsClassifier	fit	0.059	0.001	1000000	1000000	2	brute	1	5	NaN	NaN	0.106	0.002	0.560	0.001	See
31	KNeighborsClassifier	predict	0.028	0.003	1000000	1	2	brute	1	5	1.0	1.0	0.005	0.000	6.072	0.015	See
32	KNeighborsClassifier	predict	19.154	0.075	1000000	1000	2	brute	1	5	1.0	1.0	0.815	0.008	23.510	0.000	See
33	KNeighborsClassifier	fit	0.056	0.001	1000000	1000000	2	brute	1	100	NaN	NaN	0.106	0.001	0.527	0.001	See
34	KNeighborsClassifier	predict	0.027	0.002	1000000	1	2	brute	1	100	1.0	1.0	0.005	0.001	5.922	0.020	See
35	KNeighborsClassifier	predict	19.395	0.202	1000000	1000	2	brute	1	100	1.0	1.0	0.891	0.011	21.770	0.000	See

Plots

Shared hyperparameters:

	value
algorithm	brute

KNeighborsClassifier_kd_tree vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	accuracy_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KNeighborsClassifier_kd_tree	fit	2.900	0.075	1000000	1000000	10	kd_tree	-1	1	NaN	NaN	0.736	0.013	3.940	0.001	See
1	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	-1	1	1.0	1.0	0.001	0.000	4.579	0.154	See
2	KNeighborsClassifier_kd_tree	predict	0.475	0.020	1000000	1000	10	kd_tree	-1	1	1.0	1.0	0.127	0.002	3.738	0.002	See
3	KNeighborsClassifier_kd_tree	fit	2.819	0.047	1000000	1000000	10	kd_tree	-1	5	NaN	NaN	0.785	0.026	3.592	0.001	See
4	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	10	kd_tree	-1	5	1.0	1.0	0.001	0.000	3.770	0.134	See
5	KNeighborsClassifier_kd_tree	predict	0.860	0.011	1000000	1000	10	kd_tree	-1	5	1.0	1.0	0.228	0.006	3.777	0.001	See
6	KNeighborsClassifier_kd_tree	fit	2.707	0.063	1000000	1000000	10	kd_tree	-1	100	NaN	NaN	0.752	0.020	3.600	0.001	See
7	KNeighborsClassifier_kd_tree	predict	0.004	0.001	1000000	1	10	kd_tree	-1	100	1.0	1.0	0.001	0.000	3.956	0.148	See
8	KNeighborsClassifier_kd_tree	predict	2.798	0.016	1000000	1000	10	kd_tree	-1	100	1.0	1.0	0.699	0.012	4.005	0.000	See
9	KNeighborsClassifier_kd_tree	fit	2.764	0.042	1000000	1000000	10	kd_tree	1	1	NaN	NaN	0.775	0.015	3.565	0.001	See
10	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	10	kd_tree	1	1	1.0	1.0	0.001	0.000	1.501	0.185	See
11	KNeighborsClassifier_kd_tree	predict	0.758	0.011	1000000	1000	10	kd_tree	1	1	1.0	1.0	0.123	0.004	6.172	0.001	See
12	KNeighborsClassifier_kd_tree	fit	2.695	0.083	1000000	1000000	10	kd_tree	1	5	NaN	NaN	0.752	0.020	3.586	0.002	See
13	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	10	kd_tree	1	5	1.0	1.0	0.001	0.000	1.460	0.502	See
14	KNeighborsClassifier_kd_tree	predict	1.420	0.036	1000000	1000	10	kd_tree	1	5	1.0	1.0	0.231	0.007	6.140	0.001	See
15	KNeighborsClassifier_kd_tree	fit	3.005	0.121	1000000	1000000	10	kd_tree	1	100	NaN	NaN	0.790	0.012	3.806	0.002	See
16	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	10	kd_tree	1	100	1.0	1.0	0.001	0.000	2.373	0.212	See
17	KNeighborsClassifier_kd_tree	predict	5.069	0.069	1000000	1000	10	kd_tree	1	100	1.0	1.0	0.697	0.015	7.276	0.001	See
18	KNeighborsClassifier_kd_tree	fit	1.493	0.037	1000000	1000000	2	kd_tree	-1	1	NaN	NaN	0.514	0.017	2.903	0.002	See
19	KNeighborsClassifier_kd_tree	predict	0.003	0.000	1000000	1	2	kd_tree	-1	1	1.0	1.0	0.000	0.000	16.649	0.223	See
20	KNeighborsClassifier_kd_tree	predict	0.040	0.002	1000000	1000	2	kd_tree	-1	1	1.0	1.0	0.001	0.000	45.458	0.084	See
21	KNeighborsClassifier_kd_tree	fit	1.494	0.039	1000000	1000000	2	kd_tree	-1	5	NaN	NaN	0.515	0.011	2.898	0.001	See
22	KNeighborsClassifier_kd_tree	predict	0.002	0.000	1000000	1	2	kd_tree	-1	5	1.0	1.0	0.000	0.000	14.978	0.239	See
23	KNeighborsClassifier_kd_tree	predict	0.042	0.002	1000000	1000	2	kd_tree	-1	5	1.0	1.0	0.001	0.000	35.321	0.060	See
24	KNeighborsClassifier_kd_tree	fit	1.488	0.033	1000000	1000000	2	kd_tree	-1	100	NaN	NaN	0.506	0.016	2.938	0.001	See
25	KNeighborsClassifier_kd_tree	predict	0.003	0.001	1000000	1	2	kd_tree	-1	100	1.0	1.0	0.000	0.000	16.566	0.278	See
26	KNeighborsClassifier_kd_tree	predict	0.066	0.011	1000000	1000	2	kd_tree	-1	100	1.0	1.0	0.008	0.001	8.517	0.036	See
27	KNeighborsClassifier_kd_tree	fit	1.472	0.028	1000000	1000000	2	kd_tree	1	1	NaN	NaN	0.536	0.015	2.745	0.001	See
28	KNeighborsClassifier_kd_tree	predict	0.001	0.000	1000000	1	2	kd_tree	1	1	1.0	1.0	0.000	0.000	5.268	0.274	See
29	KNeighborsClassifier_kd_tree	predict	0.038	0.001	1000000	1000	2	kd_tree	1	1	1.0	1.0	0.001	0.000	46.608	0.072	See
30	KNeighborsClassifier_kd_tree	fit	1.471	0.028	1000000	1000000	2	kd_tree	1	5	NaN	NaN	0.520	0.008	2.827	0.001	See
31	KNeighborsClassifier_kd_tree	predict	0.001	0.001	1000000	1	2	kd_tree	1	5	1.0	1.0	0.000	0.000	5.699	0.630	See
32	KNeighborsClassifier_kd_tree	predict	0.042	0.001	1000000	1000	2	kd_tree	1	5	1.0	1.0	0.001	0.000	34.851	0.052	See
33	KNeighborsClassifier_kd_tree	fit	1.481	0.028	1000000	1000000	2	kd_tree	1	100	NaN	NaN	0.525	0.009	2.819	0.001	See
34	KNeighborsClassifier_kd_tree	predict	0.010	0.027	1000000	1	2	kd_tree	1	100	1.0	1.0	0.000	0.000	53.170	7.547	See
35	KNeighborsClassifier_kd_tree	predict	0.073	0.001	1000000	1000	2	kd_tree	1	100	1.0	1.0	0.009	0.001	8.432	0.005	See

Plots

Shared hyperparameters:

	value
algorithm	kd_tree

KMeans_tall vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_tall	fit	0.658	0.014	1000000	1000000	2	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	0.326	0.008	2.019	0.001	See
1	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.139	0.319	See
2	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.891	0.232	See
3	KMeans_tall	fit	0.545	0.006	1000000	1000000	2	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	0.294	0.012	1.856	0.002	See
4	KMeans_tall	predict	0.000	0.000	1000000	1	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.911	0.260	See
5	KMeans_tall	predict	0.000	0.000	1000000	1000	2	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.923	0.259	See
6	KMeans_tall	fit	7.408	0.090	1000000	1000000	100	full	k-means++	30	3	1	0.0	30.0	NaN	30.0	NaN	4.014	0.052	1.845	0.000	See
7	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.015	0.269	See
8	KMeans_tall	predict	0.001	0.000	1000000	1000	100	full	k-means++	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.034	0.192	See
9	KMeans_tall	fit	6.667	0.112	1000000	1000000	100	full	random	30	3	1	0.0	30.0	NaN	30.0	NaN	3.801	0.046	1.754	0.000	See
10	KMeans_tall	predict	0.000	0.000	1000000	1	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.712	0.202	See
11	KMeans_tall	predict	0.001	0.001	1000000	1000	100	full	random	30	3	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	3.856	1.238	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	3
max_iter	30
n_init	1
tol	0.0

KMeans_short vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	algorithm	init	max_iter	n_clusters	n_init	tol	n_iter_sklearn	adjusted_rand_score_sklearn	n_iter_daal4py	adjusted_rand_score_daal4py	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	KMeans_short	fit	0.387	0.016	10000	10000	2	full	k-means++	30	300	1	0.0	30.0	NaN	28.0	NaN	0.147	0.007	2.641	0.004	See
1	KMeans_short	predict	0.000	0.000	10000	1	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.200	0.317	See
2	KMeans_short	predict	0.001	0.000	10000	1000	2	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.278	0.052	See
3	KMeans_short	fit	0.142	0.004	10000	10000	2	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.068	0.001	2.084	0.001	See
4	KMeans_short	predict	0.000	0.000	10000	1	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.161	0.279	See
5	KMeans_short	predict	0.001	0.000	10000	1000	2	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	1.379	0.066	See
6	KMeans_short	fit	1.233	0.028	10000	10000	100	full	k-means++	30	300	1	0.0	18.0	NaN	17.0	NaN	0.579	0.022	2.130	0.002	See
7	KMeans_short	predict	0.001	0.000	10000	1	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	1.986	0.444	See
8	KMeans_short	predict	0.006	0.003	10000	1000	100	full	k-means++	30	300	1	0.0	NaN	1.0	NaN	1.0	0.001	0.000	4.284	0.212	See
9	KMeans_short	fit	0.372	0.041	10000	10000	100	full	random	30	300	1	0.0	30.0	NaN	30.0	NaN	0.297	0.035	1.251	0.026	See
10	KMeans_short	predict	0.000	0.000	10000	1	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.000	0.000	2.045	0.203	See
11	KMeans_short	predict	0.008	0.001	10000	1000	100	full	random	30	300	1	0.0	NaN	1.0	NaN	1.0	0.002	0.000	5.562	0.040	See

Plots

Shared hyperparameters:

	value
algorithm	full
n_clusters	300
max_iter	30
n_init	1
tol	0.0

LogisticRegression vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	LogisticRegression	fit	15.331	0.036	1000000	1000000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[20]	15.709	0.083	0.976	0.000	See
1	LogisticRegression	predict	0.000	0.000	1000000	1	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.396	1.107	See
2	LogisticRegression	predict	0.000	0.000	1000000	1000	100	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.000	0.000	0.840	0.147	See
3	LogisticRegression	fit	1.170	0.017	1000	1000	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	[26]	1.231	0.055	0.951	0.002	See
4	LogisticRegression	predict	0.000	0.000	1000	1	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.002	0.002	0.076	1.438	See
5	LogisticRegression	predict	0.002	0.000	1000	100	10000	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0	0	False	NaN	0.005	0.000	0.535	0.014	See

Plots

Shared hyperparameters:

	value
penalty	l2
dual	False
tol	0.0001
C	1.0
fit_intercept	True
intercept_scaling	1
class_weight	NaN
random_state	NaN
solver	lbfgs
max_iter	100
multi_class	auto
verbose	0
warm_start	False
n_jobs	NaN
l1_ratio	NaN

Ridge vs daal4py

Results

	estimator	function	mean_sklearn	stdev_sklearn	n_samples_train	n_samples	n_features	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	n_iter	mean_daal4py	stdev_daal4py	speedup	stdev_speedup	profiling
0	Ridge	fit	0.049	0.001	1000	1000	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.031	0.003	1.593	0.010	See
1	Ridge	predict	0.000	0.000	1000	1	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.604	0.623	See
2	Ridge	predict	0.000	0.000	1000	100	1000	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	1.164	0.387	See
3	Ridge	fit	0.012	0.001	10000	10000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.006	0.004	2.000	0.399	See
4	Ridge	predict	0.000	0.000	10000	1	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.668	0.609	See
5	Ridge	predict	0.000	0.000	10000	1000	100	1.0	True	True	NaN	False	NaN	auto	0.001	NaN	0.000	0.000	0.620	0.298	See

Plots

Shared hyperparameters:

	value
alpha	1.0
fit_intercept	True
normalize	False
copy_X	True
max_iter	NaN
tol	0.001
solver	auto
random_state	NaN

Environment information

{
  "system_info": {
    "python": "3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 16:22:27)  [GCC 9.3.0]",
    "executable": "/usr/share/miniconda/envs/sklbench/bin/python",
    "machine": "Linux-5.4.0-1046-azure-x86_64-with-glibc2.10"
  },
  "dependencies_info": {
    "pip": "21.1",
    "setuptools": "49.6.0.post20210108",
    "sklearn": "0.24.1",
    "numpy": "1.20.2",
    "scipy": "1.6.2",
    "Cython": null,
    "pandas": "1.2.4",
    "matplotlib": null,
    "joblib": "1.0.1",
    "threadpoolctl": "2.1.0"
  },
  "threadpool_info": [
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libopenblasp-r0.3.12.so",
      "prefix": "libopenblas",
      "user_api": "blas",
      "internal_api": "openblas",
      "version": "0.3.12",
      "num_threads": 2,
      "threading_layer": "pthreads"
    },
    {
      "filepath": "/usr/share/miniconda/envs/sklbench/lib/libgomp.so.1.0.0",
      "prefix": "libgomp",
      "user_api": "openmp",
      "internal_api": "openmp",
      "version": null,
      "num_threads": 2
    }
  ],
  "cpu_count": 2
}